Generalized fluctuation test for deciphering phenotypic switching within cell populations

破译细胞群内表型转换的广义波动测试

• 批准号: 10552300
• 负责人: Abhyudai Singh
• 金额: $ 39.14万
• 依托单位: UNIVERSITY OF DELAWARE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-01 至 2027-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10552300
• 关键词: Affect; Automobile Driving; Benchmarking; Biochemical; Biochemical Process; Biochemical Reaction; Biological Assay; Biological Process; Cell Proliferation; Cells; Coupled; DNA; DNA Sequence Alteration; Data; Data Set; Drug Tolerance; Epigenetic Process; Heterogeneity; Human; Individual; Mathematics; Measurement; Measures; Messenger RNA; Methods; Microbe; Modeling; Mutation; Nature; Nobel Prize; Noise; Phenotype; Play; Population; Predisposition; Proteins; Role; Techniques; Testing; Therapeutic; Time; Variant; Viral; Work; cancer cell; cell killing; cell type; computerized tools; drug-sensitive; environmental change; experimental study; gene product; in silico; innovation; mathematical model; response; stem cells; tool; transcriptome sequencing; translational approach

Generalized fluctuation test for deciphering phenotypic switching within cell populations The inherent probabilistic nature of biochemical reactions coupled with low-copy number components results in significant random fluctuations (noise) in mRNA/protein levels inside individual cells. How cellular biochemical processes function reliably in the face of such randomness is an intriguing fundamental problem. A long-term vision of our lab is to develop new mathematical and computational tools for studying stochastic dynamics of cellular biochemical processes, and use these tools to systematically understand how noise affects biological function and phenotype. As a consequence of noise in gene product levels, single cells within an isoclonal population can differ in their expression profile and reside in different pheno- typic states. The dynamic nature of this intercellular variation, where individual cells can transition between different states over time makes it a particularly hard phenomenon to characterize. Unexpectedly, phenotypic heterogeneity within a population can play important functional roles in diverse biological processes, from driving genetically-identical cells to different cell fates to allowing microbes and cancer cells to hedge their bets against uncertain environmental changes. The Luria-Delbrück experiment, also called the “Fluctuation Test", introduced 75 years ago, demonstrated that genetic mu- tations arise randomly in the absence of selection – rather than in response to selection – and led to a Nobel Prize. The innovation of this project is to leverage this classical experiment in conjunction with mathematical modeling to char- acterize reversible and irreversible switching between cell states. The key advantage of the proposed method is that it is general enough to be applied to any proliferating cell type, and only involves making a single endpoint measurement. This is especially important for scenarios where a measurement involves killing the cell (for example, assaying whether a bacte- rial cell is in a drug-sensitive or drug-tolerant state or doing RNA-sequencing), and hence the state of the same cell cannot be measured at different time points. The project will develop mathematical tools for characterizing phenotypic switching between an arbitrary number of states using the fluctuation test, and such techniques will for the first time differentiate between an irreversible cell-state transition via genetic alterations vs. a reversible epigenetic transition. These tools will be first benchmarked with in-silico generated data and then applied on experimental datasets investigating diverse prob- lems, including characterizing drug-tolerant states in bacterial/fungal cells, understanding differences in viral susceptibility between single human cells within the same clonal population, and uncovering the transient dynamics of stem cell states that bias individual cells to different differentiation fates. Our preliminary work reveals plasticity in drug-tolerant states in bacterial, fungal, and cancer cells with different inheritance timescales. To understand the origins of cell states, the project will develop computational tools for inferring causal interaction networks from single-cell expression data. These tools will uncover how network topologies change across cell states and modeling the stochastic dynamics of underlying biochemical networks will mechanistically capture transitions between states. Overall, tools developed through this project will result in a fundamental understanding of how single-cell difference arises from stochastic epigenetic processes without any changes to DNA, and drive translational approaches to perturb cell states for therapeutic benefit.

用于破译细胞群体内表型转换的广义波动检验 生物化学反应的固有概率性质加上低拷贝数成分导致显著的 单个细胞内mRNA/蛋白质水平的随机波动（噪音）。细胞生物化学过程如何运作 在这种随机性面前，可靠性是一个令人感兴趣的基本问题。我们实验室的长期愿景是开发 新的数学和计算工具，用于研究细胞生物化学过程的随机动力学，并使用这些 系统地了解噪音如何影响生物功能和表型的工具。由于基因中的噪音 产物水平，同克隆群体中的单个细胞的表达谱可能不同，并存在于不同的表型中。 典型国家这种细胞间变化的动态性质，其中单个细胞可以在不同的细胞之间转换， 状态随时间的变化使得它成为一种特别难以描述的现象。出乎意料的是，表型异质性 在不同的生物过程中发挥重要的功能作用，从驱动基因相同的 不同的细胞命运，让微生物和癌细胞对冲他们的赌注不确定的环境变化。 75年前推出的Luria-Delbrück实验，也被称为“波动测试”，证明了基因突变是一种基因突变。 在没有选择的情况下随机出现的情况-而不是对选择的反应-并导致了诺贝尔奖。的 该项目的创新之处在于利用这一经典实验与数学建模相结合， 实现电池状态之间的可逆和不可逆切换。所提出的方法的主要优点是， 一般足以应用于任何增殖细胞类型，并且仅涉及进行单一终点测量。这 对于测量涉及杀死细胞的情况（例如，测定细菌是否存在）尤其重要。 细胞处于药物敏感或药物耐受状态或进行RNA测序），因此同一细胞的状态不能 在不同的时间点测量。该项目将开发表征表型转换的数学工具 在任意数量的状态之间使用波动测试，这种技术将首次区分 通过遗传改变的不可逆细胞状态转变与可逆表观遗传转变之间的关系。这些工具 将首先使用计算机生成的数据进行基准测试，然后应用于研究不同问题的实验数据集， lems，包括表征细菌/真菌细胞中的耐药性状态，了解病毒易感性的差异 在同一克隆群体中的单个人类细胞之间，并揭示干细胞状态的瞬态动力学 使单个细胞偏向不同的分化命运。我们的初步工作揭示了药物耐受状态的可塑性 在不同遗传时间尺度的细菌、真菌和癌细胞中。为了理解细胞状态的起源， 该项目将开发计算工具，用于从单细胞表达数据推断因果相互作用网络。这些 工具将揭示网络拓扑如何在单元状态之间变化，并对底层的随机动态进行建模。 生物化学网络将机械地捕获状态之间的转换。总体而言，通过该项目开发的工具 将导致对单细胞差异如何从随机表观遗传过程中产生的基本理解 而不对DNA进行任何改变，并驱动翻译方法来扰乱细胞状态以获得治疗贝内。